12 GB VRAM (+2)
B
This setup is broadly balanced for this model.47.1 tok/s decode
~$329 MSRP
Can Yi 1.5 9B run on RTX 3080 10GB?
YES — Tight Fit
B58Good
○Estimated from fit model
Yi 1.5 9B needs ~9.2 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~114 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
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Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 9.2 GB, 114.4 tok/s, Tight fit
9.2 GB required10.0 GB available
92% VRAM used
Fit status
Tight fit
Decode
114.4 tok/s
TTFT
1692 ms
Safe context
4K
Memory
9.2 GB / 10.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsYi 1.5 9B on RTX 3080 10GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 114.4 tok/s decode · 1.7s TTFT (warm) · 286 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 105.2 tok/s | 1004 ms | 4K |
| Coding | B | Tight fit | 114.4 tok/s | 1692 ms | 4K |
| Agentic Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 75.6 tok/s | 3724 ms | 4K |
| Reasoning | B | Tight fit | 114.4 tok/s | 2000 ms | 4K |
| RAG | C | Runs with offload (needs ~0.3 GB host RAM) | 75.6 tok/s | 4655 ms |
Quantization options
How Yi 1.5 9B (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B56 |
Q3_K_S | 3 | 4.4 GB | Low | B57 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startUpgrade options